FastTTS: Accelerating Test-Time Scaling for Edge LLM Reasoning
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arXiv
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| Auteurs principaux: | , , , , , , |
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| Format: | Preprint |
| Publié: |
2025
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| _version_ | 1866912865203519488 |
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| author | Chen, Hao Mark Mo, Zhiwen Lu, Guanxi Liang, Shuang Ma, Lingxiao Luk, Wayne Fan, Hongxiang |
| author_facet | Chen, Hao Mark Mo, Zhiwen Lu, Guanxi Liang, Shuang Ma, Lingxiao Luk, Wayne Fan, Hongxiang |
| contents | Recent advances in reasoning Large Language Models (LLMs) are driving the emergence of agentic AI systems. Edge deployment of LLM agents near end users is increasingly necessary to protect data privacy, enable offline use, and provide responsive interaction with local context. However, strict memory constraints on edge devices limit deployment to smaller LLMs, whose reasoning capabilities are much weaker than those of large cloud models, hindering practical deployment of edge agentic AI. Test-Time Scaling (TTS) offers a promising solution by allocating more compute during inference to enhance the reasoning capability of edge LLMs. However, current TTS methods introduce heavy hardware performance overhead on resource-constrained devices, making them impractical for real applications. To address this challenge, we present FastTTS, a serving system that enables fast and efficient TTS for memory-constrained LLM reasoning. After analyzing common patterns across various TTS methods and identifying their performance bottlenecks, we introduce three novel techniques: i) Speculative Beam Extension, which mitigates system stragglers caused by irregular reasoning paths, ii) Asymmetric Multi-Model Memory Allocation, which dynamically balances memory usage between token generation and reasoning-step verification, and iii) Dynamic Prefix-Aware Scheduling, which optimizes reasoning execution to maximize KV-cache reuse across search paths. FastTTS offers a plug-and-play third-party library on top of vLLM, enabling edge LLMs on a single consumer GPU to match cloud-model accuracy and cloud-measured latency. Comprehensive evaluation shows that FastTTS achieves an average 2.2x higher goodput and reduces latency by 38%--68% compared to the vLLM baseline; it pushes the boundaries of low-latency TTS on memory-constrained edge devices and highlights the potential for democratizing agentic AI. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_00195 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | FastTTS: Accelerating Test-Time Scaling for Edge LLM Reasoning Chen, Hao Mark Mo, Zhiwen Lu, Guanxi Liang, Shuang Ma, Lingxiao Luk, Wayne Fan, Hongxiang Machine Learning Recent advances in reasoning Large Language Models (LLMs) are driving the emergence of agentic AI systems. Edge deployment of LLM agents near end users is increasingly necessary to protect data privacy, enable offline use, and provide responsive interaction with local context. However, strict memory constraints on edge devices limit deployment to smaller LLMs, whose reasoning capabilities are much weaker than those of large cloud models, hindering practical deployment of edge agentic AI. Test-Time Scaling (TTS) offers a promising solution by allocating more compute during inference to enhance the reasoning capability of edge LLMs. However, current TTS methods introduce heavy hardware performance overhead on resource-constrained devices, making them impractical for real applications. To address this challenge, we present FastTTS, a serving system that enables fast and efficient TTS for memory-constrained LLM reasoning. After analyzing common patterns across various TTS methods and identifying their performance bottlenecks, we introduce three novel techniques: i) Speculative Beam Extension, which mitigates system stragglers caused by irregular reasoning paths, ii) Asymmetric Multi-Model Memory Allocation, which dynamically balances memory usage between token generation and reasoning-step verification, and iii) Dynamic Prefix-Aware Scheduling, which optimizes reasoning execution to maximize KV-cache reuse across search paths. FastTTS offers a plug-and-play third-party library on top of vLLM, enabling edge LLMs on a single consumer GPU to match cloud-model accuracy and cloud-measured latency. Comprehensive evaluation shows that FastTTS achieves an average 2.2x higher goodput and reduces latency by 38%--68% compared to the vLLM baseline; it pushes the boundaries of low-latency TTS on memory-constrained edge devices and highlights the potential for democratizing agentic AI. |
| title | FastTTS: Accelerating Test-Time Scaling for Edge LLM Reasoning |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2509.00195 |